Q1. You have two DataFrames: df1 (customer_id, name) and df2 (customer_id, purchase_amount). Merge them to get customer names with purchases.
merged = pd.merge(df1, df2, on=''customer_id'', how=''inner'')how can be ''inner'', ''left'', ''right'', ''outer''. This is SQL-like join. Also use .join() or .merge().Q2. Concatenate two DataFrames with the same columns vertically (stack rows) using pd.concat.
combined = pd.concat([df1, df2], axis=0, ignore_index=True)ignore_index resets index. For columns, axis=1.Q3. Left join df1 (products) with df2 (sales) on product_id, keep all products even if no sales. Fill missing sales with 0.
merged = pd.merge(df1, df2, on=''product_id'', how=''left'')
merged[''sales''] = merged[''sales''].fillna(0)Q4. Merge two DataFrames on multiple keys: (year, month).
merged = pd.merge(df1, df2, on=[''year'',''month''], how=''inner'')Q5. Perform an outer join between df1 and df2 and indicate which source each row came from using the indicator parameter.
merged = pd.merge(df1, df2, on=''id'', how=''outer'', indicator=True)